A deep learning model for the detection of both advanced and early glaucoma using fundus photography

• Published in: PloS one Vol. 13; no. 11; p. e0207982
• Main Authors: Ahn, Jin Mo, Kim, Sangsoo, Ahn, Kwang-Sung, Cho, Sung-Hoon, Lee, Kwan Bok, Kim, Ungsoo Samuel
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 27.11.2018; Public Library of Science (PLoS)
• Subjects: Analysis; Artificial neural networks; Biology and Life Sciences; Case studies; Classification; Computer and Information Sciences; Cross-Sectional Studies; Data transfer (computers); Datasets; Deep Learning; Diabetic retinopathy; Diagnosis; Diagnostic Techniques, Ophthalmological; Disease Progression; Fundus Oculi; Glaucoma; Glaucoma - diagnostic imaging; Humans; Image Interpretation, Computer-Assisted - methods; Learning algorithms; Logistic Models; Machine learning; Macular degeneration; Medicine and Health Sciences; Model accuracy; Neural networks; Photography; Physical Sciences; Research and Analysis Methods; ROC Curve; Training; Japan

To build a deep learning model to diagnose glaucoma using fundus photography. Cross sectional case study Subjects, Participants and Controls: A total of 1,542 photos (786 normal controls, 467 advanced glaucoma and 289 early glaucoma patients) were obtained by fundus photography. The whole dataset of...